瓶颈
计算机科学
元启发式
计算复杂性理论
计算科学
模式(遗传算法)
布谷鸟搜索
熵(时间箭头)
算法
统计物理学
理论计算机科学
物理
粒子群优化
热力学
机器学习
嵌入式系统
作者
Rahul Singh,Aayush Sharma,Prashant Singh,Ganesh Balasubramanian,D. D. Johnson
标识
DOI:10.1038/s43588-020-00006-7
摘要
With huge design spaces for unique chemical and mechanical properties, we remove a roadblock to computational design of {high-entropy alloys} using a metaheuristic hybrid Cuckoo Search (CS) for "on-the-fly" construction of Super-Cell Random APproximates (SCRAPs) having targeted atomic site and pair probabilities on arbitrary crystal lattices. Our hybrid-CS schema overcomes large, discrete combinatorial optimization by ultrafast global solutions that scale linearly in system size and strongly in parallel, e.g. a 4-element, 128-atom model [a $10^{73+}$ space] is found in seconds -- a reduction of 13,000+ over current strategies. With model-generation eliminated as a bottleneck, computational alloy design can be performed that is currently impossible or impractical. We showcase the method for real alloys with varying short-range order. Being problem-agnostic, our hybrid-CS schema offers numerous applications in diverse fields.
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